Cross-cloud platform disaster recovery data migration recovery method and system

Abstract

The invention relates to the technical field of computers, and provides a cross-cloud-platform disaster recovery data migration recovery method and system, and the method comprises the steps: for each type of business data to be subjected to disaster recovery in a main device, according to the data characteristic information of each type of business data and the platform characteristic information of each cloud platform, carrying out the disaster recovery data migration recovery according to the data characteristic information of each type of business data; determining at least two target cloud platforms of each type of business data; transmitting each type of service data to at least two corresponding target cloud platforms for storage; when the main device breaks down, for each type of service data, determining an optimal data recovery cloud platform according to platform state information of each cloud platform in the at least two target cloud platforms corresponding to the service data; and based on each optimal data recovery cloud platform, recovering each type of service data to standby equipment of the main equipment. According to the embodiment of the invention, disaster recovery data migration and recovery are carried out through the cross-cloud platform, and the stability and reliability of enterprise business operation are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a disaster recovery data migration and restoration method and system across cloud platforms. Background Art

[0002] In today's digital age, data is crucial for the operation of enterprises and organizations. To cope with various risks that may lead to data loss or system failures, disaster recovery data migration and restoration methods have emerged. Currently, the widely adopted disaster recovery data migration and restoration methods are mainly based on a single cloud platform. To a certain extent, this method can ensure the security and recoverability of data, providing a basic guarantee for the normal operation of enterprises.

[0003] However, since the entire disaster recovery process depends on a single cloud platform, when this cloud platform experiences a serious failure (such as the cloud platform suffering from natural disasters, cyber attacks resulting in the paralysis of the cloud platform, etc.), the disaster recovery data migration and restoration may not be able to proceed normally. Once there are problems with the single cloud platform where the primary device and the disaster recovery server are located, the backup data may not be able to be transmitted and restored normally, leading to the risk of business data loss and business interruption for enterprises, thus affecting the stability and reliability of enterprise business operations. Summary of the Invention

[0004] The present invention provides a disaster recovery data migration and restoration method and system across cloud platforms to improve the stability and reliability of enterprise business operations.

[0005] In a first aspect, the present invention provides a disaster recovery data migration and restoration method across cloud platforms, including: For each type of business data to be disaster-recovered in the primary device, at least two target cloud platforms for each type of business data are determined according to the data characteristic information of each type of business data and the platform characteristic information of each cloud platform; Each type of business data is transmitted to its corresponding at least two target cloud platforms for storage; When a failure occurs in the primary device, for each type of business data, an optimal data recovery cloud platform is determined according to the platform status information of each cloud platform among its corresponding at least two target cloud platforms; Based on each optimal data recovery cloud platform, each type of business data is restored to the standby device of the primary device.

[0006] In a second aspect, the present invention further provides a disaster recovery data migration and restoration system across cloud platforms, which is applied to the disaster recovery data migration and restoration method across cloud platforms as described in the first aspect; the disaster recovery data migration and restoration system across cloud platforms includes: A cross-cloud platform determination module, configured to determine at least two target cloud platforms for each type of business data to be disaster-recovered in the primary device according to the data characteristic information of each type of business data and the platform characteristic information of each cloud platform; A disaster recovery data migration module, configured to transmit each type of service data to at least two corresponding target cloud platforms for storage; A source cloud platform positioning module, configured to, when a main device fails, for each type of service data, determine an optimal data recovery cloud platform according to the platform status information of each cloud platform among at least two corresponding target cloud platforms; A disaster recovery data recovery module, configured to recover each type of service data to a standby device of the main device based on each optimal data recovery cloud platform.

[0007] In a third aspect, the present invention further provides an electronic device, including: a memory, configured to store a computer software program; a processor, configured to read and execute the computer software program, so as to implement the cross-cloud-platform disaster recovery data migration and recovery method as described in any one of the above.

[0008] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, in which a computer software program is stored, and when the computer software program is executed by a processor, the cross-cloud-platform disaster recovery data migration and recovery method as described in any one of the above is implemented.

[0009] In a fifth aspect, the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the cross-cloud-platform disaster recovery data migration and recovery method as described in any one of the above is implemented.

[0010] The cross-cloud-platform disaster recovery data migration and recovery method provided by the embodiments of the present invention selects at least two target cloud platforms as disaster recovery storage locations for each type of service data, avoiding the risk of centralized storage of service data in a single cloud platform. In the subsequent process, even if a certain cloud platform fails, the service data can still be recovered from other cloud platforms, avoiding the problem of service data loss and improving the reliability of enterprise business operations. On the other hand, when the main device fails, the optimal data recovery cloud platform is selected from multiple target cloud platforms through the platform status information to recover each type of service data, avoiding the risk of service interruption, reducing the time in the service data recovery process, and improving the stability of enterprise business operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a flowchart of the cross-cloud-platform disaster recovery data migration and recovery method provided by the embodiments of the present invention; Figure 2 is a structural diagram of the cross-cloud-platform disaster recovery data migration and recovery system provided by the embodiments of the present invention; Figure 3 is an embodiment diagram of the electronic device provided by the embodiments of the present invention; Figure 4This is an embodiment diagram of the computer-readable storage medium provided by the embodiments of the present invention. Detailed implementation manners

[0012] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0013] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0014] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or more advantageous than other embodiments. In order to enable any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0015] Optionally, refer to Figure 1 , Figure 1 is a flowchart of the disaster recovery data migration and restoration method across cloud platforms provided by the present invention. In the embodiments of the present invention, the execution subject of the disaster recovery data migration and restoration method across cloud platforms is the disaster recovery data migration and restoration system across cloud platforms. One form of the disaster recovery data migration and restoration system across cloud platforms is a data management system. Therefore, the disaster recovery data migration and restoration method across cloud platforms includes: Step 10, for each type of business data to be disaster-recovered in the primary device, at least two target cloud platforms for each type of business data are determined according to the data characteristic information of each type of business data and the platform characteristic information of each cloud platform.

[0016] Optionally, the data characteristic information in the embodiments of the present invention includes service type, importance level, and change frequency, and the platform characteristic information includes storage capacity, read / write speed, and security level. Among them, the service type determines the application scenario and access mode of the data, the importance level reflects the impact degree of data loss or damage on the service, and the change frequency reflects the frequency of data updates. The storage capacity determines the amount of data that can be accommodated, the read / write speed affects the access efficiency of the data, and the security level ensures the storage security of the data.

[0017] Therefore, for each master device, the data management system acquires each type of service data that needs disaster recovery in the master device, where the service data includes customer information, order data, log files, etc. Further, the data management system matches at least two target cloud platforms for each type of service data in the cloud platform library according to the data characteristic information of each type of service data and the platform characteristic information of each cloud platform.

[0018] In one embodiment, for the service data of financial transaction data, the service type is high-real-time transaction processing, the importance level is extremely high, and the change frequency is relatively high. The cloud platform library includes cloud platform A, cloud platform B, and cloud platform C, and their platform characteristics are as follows: Cloud platform A: storage capacity of 100TB, read / write speed of 10GB / s, and high security level. Cloud platform B: storage capacity of 50TB, read / write speed of 5GB / s, and extremely high security level. Cloud platform C: storage capacity of 200TB, read / write speed of 3GB / s, and medium security level. First, according to the extremely high importance level, cloud platforms with a security level of high or above are screened out, namely cloud platform A and cloud platform B. Then, considering the read / write speed, due to the high real-time requirement of financial transaction data, the faster the read / write speed, the better. The read / write speed of cloud platform A is more optimal. Looking at the storage capacity again, although the current amount of financial transaction data is small, considering future growth, the large capacity of cloud platform C can be used as a long-term storage option. Finally, cloud platform A and cloud platform C are determined as the target cloud platforms for financial transaction data.

[0019] Step 20: Transmit each type of service data to its corresponding at least two target cloud platforms for storage.

[0020] Further, the data management system encapsulates and packages each type of service data according to the interface specification of the target cloud platform, and transmits the encapsulated and packaged each type of service data to the target cloud platform for storage.

[0021] It should be noted that before data transmission, in order to ensure data security, the data management system encrypts each type of service data, and then transmits the encrypted data of each type of service data to at least two target cloud platforms corresponding to each type of service data for storage. The specific data encryption process is described in detail in steps 21 to 23.

[0022] Meanwhile, during the data transmission process, it is necessary to ensure the integrity and accuracy of the data. Therefore, after receiving the transmitted encrypted data, the target cloud platform decrypts the encrypted data and stores it. Among them, the decryption process and the encryption process are inverse processes, which will not be elaborated here. After the storage is completed, the target cloud platform verifies the integrity of the data to determine whether the data is damaged during the transmission and storage processes, and returns a data transmission feedback signal to the data management system. Therefore, the data management system receives the data transmission feedback signal fed back by the target cloud platform in real time. If the data transmission feedback signal indicates that the data is damaged, each type of service data is re-encrypted and then re-transmitted to at least two target cloud platforms corresponding to each type of service data for storage.

[0023] Continuing with the financial transaction data as an example, the data management system first packs the financial transaction data and encapsulates it according to the interface specifications of Cloud Platform A and Cloud Platform C. Through the encrypted channel, the data is transmitted to Cloud Platform A and Cloud Platform C simultaneously using multi-threading technology.

[0024] Step 30, when the primary device fails, for each type of service data, the optimal data recovery cloud platform is determined according to the platform status information of each cloud platform among the at least two target cloud platforms corresponding to it.

[0025] Optionally, the platform status information in the embodiments of the present invention includes the server load status, the network latency status, and the data backup integrity status. Among them, the server load status represents the proportion of the resources already used by the current server in the total resources, which affects the ability of the cloud platform to process data recovery requests. The network latency status represents the average network latency time between the cloud platform and the failed primary device, which determines the speed of data transmission back to the standby device. The data backup integrity status represents the difference rate between the backup data and the latest service data, ensuring that the recovered data is the latest and complete. Therefore, when the primary device fails, for each type of service data, the data management system evaluates the capabilities of each cloud platform according to the platform status information of each cloud platform in the target cloud platform, obtains the data recovery capabilities of each cloud platform, and determines the cloud platform with the maximum data recovery capability as the optimal data recovery cloud platform for each type of service data, as specifically described in Steps 301 to 304.

[0026] Continuing with the above embodiment, for the target cloud platform A and the target cloud platform C corresponding to the financial transaction data, their platform status information is as follows: Target cloud platform A: The server load status is 60%, the network latency status is 50 ms, and the data backup integrity status is 99%. Target cloud platform C: The server load status is 30%, the network latency status is 80 ms, and the data backup integrity status is 95%. First, evaluate the server load. The lower the load, the stronger the processing ability. The load of target cloud platform C is lower. For network latency, the lower the better. The latency of target cloud platform A is lower. In terms of data backup integrity, target cloud platform A is higher. For example, it can be calculated that the data recovery ability of target cloud platform A is higher. Therefore, target cloud platform A is determined as the optimal data recovery cloud platform.

[0027] Step 40, based on each optimal data recovery cloud platform, restore each type of business data to the standby device of the primary device.

[0028] Furthermore, for each type of business data, the data management system obtains the business data from the optimal data recovery cloud platform and restores the business data to the standby device connected to the primary device according to the data format and storage structure of the standby device. Of course, the business data can also be restored to the repaired primary device. During the writing process, data verification is required to ensure the accuracy of each piece of data. For example, the CRC verification algorithm is used to verify the transmitted data block. If the verification fails, the data block is retransmitted. Once all the data is restored, the standby device immediately takes over the business to ensure business continuity.

[0029] In the embodiment of the present invention, at least two target cloud platforms are selected as disaster recovery storage locations for each type of business data, avoiding the risk of centralized storage of business data in a single cloud platform. In the subsequent process, even if a certain cloud platform fails, the business data can still be restored from other cloud platforms, avoiding the problem of business data loss and improving the reliability of enterprise business operations. On the other hand, when the primary device fails, the optimal data recovery cloud platform is selected from multiple target cloud platforms through the platform status information to restore each type of business data, avoiding the risk of business interruption, reducing the time during the business data recovery process, and improving the stability of enterprise business operations.

[0030] In one embodiment, the descriptions of steps 101 to 104 are as follows: Step 101, based on the security level of each cloud platform, combine the business type and importance of each type of business data for matching, and determine the first candidate cloud platforms in the cloud platform library.

[0031] Optionally, for each type of business data, the data management system screens out the cloud platforms with security levels that meet the importance level from the cloud platform library according to the security level of each cloud platform in combination with the business type and importance, and determines them as the first candidate cloud platforms for each type of business data.

[0032] Among them, different business types have different requirements for data security. Data with a high degree of importance requires a higher level of security guarantee. The security level can be divided into several levels: low, medium, high, and extremely high.

[0033] In one embodiment, there are three types of business data: financial transaction data (the business type is high-real-time transaction processing, and the degree of importance is extremely high), ordinary office document data (the business type is daily office work, and the degree of importance is medium), and video surveillance data (the business type is surveillance storage, and the degree of importance is low). In the cloud platform library, there are cloud platform A (security level: high), cloud platform B (security level: extremely high), cloud platform C (security level: medium), and cloud platform D (security level: low). For financial transaction data, due to its extremely high degree of importance, only cloud platforms with a security level of high or above meet the requirements. Therefore, the first candidate cloud platforms are cloud platform A and cloud platform B. For ordinary office document data, with a medium degree of importance, cloud platforms with a security level of medium or above meet the conditions. The first candidate cloud platforms are cloud platform A, cloud platform B, and cloud platform C. For video surveillance data, with a low degree of importance, cloud platforms with a security level of low or above are all optional. The first candidate cloud platforms are cloud platform A, cloud platform B, cloud platform C, and cloud platform D.

[0034] Step 102: Based on the read / write speed of each cloud platform and the change frequency of each type of business data, perform matching to determine the second candidate cloud platforms among the first candidate cloud platforms.

[0035] Furthermore, based on the first candidate cloud platforms, the data management system performs matching and screening according to the read / write speed of the cloud platform and the change frequency of the business data to determine the second candidate cloud platforms among the first candidate cloud platforms. Among them, business data with a high change frequency requires a cloud platform with a fast read / write speed to ensure the timely update and acquisition of data. The read / write speed can be measured by the amount of data read and written per second, and the change frequency can be represented by the number of data updates per unit time.

[0036] Continuing with the above business data as an example, the change frequency of financial transaction data is 100 times per minute, the change frequency of ordinary office document data is 10 times per day, and the change frequency of video surveillance data is 1 time per hour. The read / write speed of the first candidate cloud platform A is 10 GB / s, the read / write speed of cloud platform B is 5 GB / s, the read / write speed of cloud platform C is 3 GB / s, and the read / write speed of cloud platform D is 1 GB / s. Set the minimum read / write speed threshold , according to the empirical formula , is the empirical coefficient, which is set to 0.1 GB / s / time here. For financial transaction data, due to its high change frequency, the faster the read / write speed, the better. The minimum read / write speed threshold is 。Therefore, for financial transaction data, only cloud platform A meets the requirements, and the second candidate cloud platform is cloud platform A. For ordinary office document data, the minimum read / write speed threshold , cloud platforms A, B, and C all meet the requirements, and the second candidate cloud platforms are cloud platforms A, B, and C. For video surveillance data, the minimum read / write speed threshold , cloud platforms A, B, C, and D all meet the requirements, and the second candidate cloud platforms are cloud platforms A, B, C, and D.

[0037] Step 103: Based on the storage capacity of each cloud platform, screen out the third candidate cloud platforms from the second candidate cloud platforms.

[0038] Furthermore, for each type of business data, the data management system estimates the business data volume and analyzes the growth trend based on the current data volume of the business data and the historical estimated annual growth rate, and determines the storage capacity required for the business data. Further, the data management system screens out the third candidate cloud platforms that meet the storage capacity required for the business data from the second candidate cloud platforms according to the storage capacity of each cloud platform in the second candidate cloud platforms, where the storage capacity can be expressed in bytes.

[0039] In an embodiment, the current data volume of financial transaction data is 20TB, and it is expected to grow by 20% annually; the current data volume of ordinary office document data is 10TB, and it is expected to grow by 10% annually; the current data volume of video surveillance data is 50TB, and it is expected to grow by 5% annually. For the convenience of understanding, taking cloud platforms A, B, C, and D included in the second candidate cloud platforms as an example, the storage capacity of cloud platform A is 100TB, the storage capacity of cloud platform B is 50TB, the storage capacity of cloud platform C is 30TB, and the storage capacity of cloud platform D is 15TB. For financial transaction data, the data volume after 3 years is expected to be , both cloud platforms A and B can meet the current and future 3-year requirements, and the third candidate cloud platforms are cloud platforms A and B. For ordinary office document data, the data volume after 3 years is expected to be , cloud platforms A, B, C, and D can all meet the requirements, and the third candidate cloud platforms are cloud platforms A, B, C, and D. For video surveillance data, the data volume after 3 years is expected to be , only cloud platform A can meet the requirements, and the third candidate cloud platform is cloud platform A.

[0040] Step 104: Based on the coupling degree of each cloud platform pair in the third candidate cloud platforms, determine at least two target cloud platforms for each type of business data; the coupling degree of the cloud platform pair is determined based on the business association information and resource sharing interaction information of the cloud platform pair.

[0041] Further, the data management system obtains the business association information and resource sharing and interaction information of each pair of cloud platforms in the third candidate cloud platforms, and calculates the coupling degree of each pair of cloud platforms in the third candidate cloud platforms according to the business association information and the resource sharing and interaction information. Among them, the business association information includes whether the business types served by the cloud platforms are similar, and the resource sharing and interaction information includes whether there is data sharing, computing resource borrowing, etc. between the cloud platforms. The higher the coupling degree, the better the synergy between the two cloud platforms in terms of business and resources. The specific formula for the coupling degree is as follows: Among them, represents the coupling degree, represents the business association information, represents the resource sharing and interaction information.

[0042] Further, the data management system determines at least two final target cloud platforms according to the coupling degree of each pair of cloud platforms in the third candidate cloud platforms, as specifically described in steps 1041 to 1044.

[0043] The business association information includes whether the business types served by the cloud platforms are similar, and the resource sharing and interaction information includes whether there is data sharing, computing resource borrowing, etc. between the cloud platforms. The higher the coupling degree, the better the synergy between the two cloud platforms in terms of business and resources.

[0044] The embodiments of the present invention can accurately match at least two target cloud platforms for each type of business data. The target cloud platforms can better meet the characteristic requirements of the business data in terms of security, read and write speed, storage capacity, and synergy, providing a solid foundation for subsequent data transmission, storage, and data recovery in case of main device failure, improving the reliability and effectiveness of the entire data disaster recovery process, and thus improving the stability and reliability of enterprise business operations.

[0045] In one embodiment, the descriptions of steps 1041 to 1044 are as follows: Step 1041, taking any first cloud platform in the third candidate cloud platforms as the initial clustering center, classifying the second cloud platforms in the third candidate cloud platforms into the cluster corresponding to the initial clustering center, and obtaining the initial clustering result of the first cloud platform.

[0046] Optionally, the data management system randomly selects a cloud platform from the third candidate cloud platforms as the first cloud platform, sets this first cloud platform as the initial clustering center, and then traverses the other cloud platforms (i.e., the second cloud platforms) in the third candidate cloud platforms except the first cloud platform. For each second cloud platform, calculate the coupling degree between the second cloud platform and the first cloud platform.

[0047] Further, if the coupling degree between the second cloud platform and the first cloud platform is less than or equal to a preset coupling threshold, the data management system classifies the second cloud platform into the cluster corresponding to the first cloud platform to obtain an initial clustering result of the first cloud platform. The coupling degree reflects the tightness of the business association and resource sharing interaction between the first cloud platform and the second cloud platform. The preset coupling threshold is a standard set according to business requirements and experience for determining whether the association between cloud platforms is close enough to be classified into one category.

[0048] In one embodiment, the third candidate cloud platforms are Cloud Platform A, Cloud Platform B, Cloud Platform C, and Cloud Platform D. Cloud Platform A is the first cloud platform, and the preset coupling threshold is 0.6. The calculated coupling degree between Cloud Platform B and Cloud Platform A is 0.5, the coupling degree between Cloud Platform C and Cloud Platform A is 0.7, and the coupling degree between Cloud Platform D and Cloud Platform A is 0.4. Since the coupling degrees of Cloud Platform B and Cloud Platform D with Cloud Platform A are less than or equal to 0.6, the initial clustering result of Cloud Platform A includes Cloud Platform A, Cloud Platform B, and Cloud Platform D.

[0049] Step 1042: Use the unclustered cloud platforms among the third candidate cloud platforms as new clustering centers.

[0050] Further, after completing the initial clustering of the first cloud platform, the data management system selects a cloud platform from the third candidate cloud platforms that did not participate in the clustering of the first cloud platform and uses it as a new clustering center. Therefore, it can be understood that the unclustered cloud platforms are the cloud platforms among the third candidate cloud platforms except the first cloud platform and the second cloud platform. Further, the data management system uses the unclustered cloud platforms as new clustering starting points to construct another clustering set for further classifying the cloud platforms and finding a cloud platform set that is different from the clustering of the first cloud platform but has similar characteristics (measured by the coupling degree). Continuing the above embodiment, Cloud Platform C was not included in the initial clustering of Cloud Platform A, so Cloud Platform C is selected as the new clustering center.

[0051] Step 1043: Classify the third cloud platforms in the second cloud platform into the cluster corresponding to the new clustering center to obtain a first raw clustering result of the unclustered cloud platforms, and remove the fourth cloud platforms in the third cloud platforms from the initial clustering result to obtain a second raw clustering result of the first cloud platform.

[0052] Further, the data management system traverses the second cloud platform again (i.e., the cloud platforms in the initial clustering of the first cloud platform except the first cloud platform). For each cloud platform in the second cloud platform, the data management system calculates the coupling degree between each cloud platform and the new clustering center (unclustered cloud platform).

[0053] Further, if there is a third cloud platform in the second cloud platform, where the coupling degree between the third cloud platform and the unclustered cloud platforms is less than or equal to a preset coupling threshold, the data management system classifies the third cloud platform into the cluster corresponding to the new clustering center to obtain the first original clustering result of the unclustered cloud platforms.

[0054] Further, for each cloud platform in the third cloud platform, calculate the coupling degree deviation value between the coupling degree of each cloud platform with the first cloud platform and the coupling degree with the unclustered cloud platforms. If there is a fourth cloud platform in the third cloud platform, and the coupling degree deviation value between the coupling degree of the fourth cloud platform with the first cloud platform and the coupling degree with the unclustered cloud platforms is greater than a preset deviation threshold, the data management system removes the fourth cloud platform from the initial clustering result of the first cloud platform to obtain the second original clustering result of the first cloud platform, where the preset deviation threshold is used to determine whether the attribution tendency of a cloud platform between two clustering centers is obvious enough to decide whether to adjust its clustering attribution.

[0055] Continuing with the above embodiment, the preset deviation threshold is 0.2. Calculate the coupling degree between cloud platform B and cloud platform C as 0.55, and the coupling degree between cloud platform D and cloud platform C as 0.5. Since the coupling degrees of cloud platform B and cloud platform D with cloud platform C are less than or equal to 0.6, the first original clustering result of cloud platform C includes cloud platform C, cloud platform B, and cloud platform D. Calculate the coupling degree deviation value between the coupling degree of cloud platform B with cloud platform A and the coupling degree with cloud platform C as |0.5 - 0.55| = 0.05, which is less than 0.2, so cloud platform B remains in the cluster of cloud platform A. The coupling degree deviation value between the coupling degree of cloud platform D with cloud platform A and the coupling degree with cloud platform C is |0.4 - 0.5| = 0.1, which is less than 0.2, so cloud platform D also remains in the cluster of cloud platform A. Therefore, the second original clustering result of cloud platform A is still cloud platform A, cloud platform B, and cloud platform D.

[0056] Step 1044, determine at least two target cloud platforms for each type of business data based on the first original clustering result and the second original clustering result.

[0057] Further, the data management system determines at least two target cloud platforms for each type of business data according to the first original clustering result and the second original clustering result, as specifically described in steps 10441 to 10445.

[0058] The embodiments of the present invention can select at least two cloud platforms with different coupling degree characteristics but capable of meeting business requirements for each type of business data, so that during the disaster recovery process, the business data can be stored and restored based on cloud platforms with different characteristics, improving the flexibility and reliability of the disaster recovery solution, reducing the data risk caused by the failure or performance problems of a single cloud platform, and thus better ensuring the continuity of business and the security of data, thereby improving the stability and reliability of enterprise business operations.

[0059] In one embodiment, the descriptions of steps 10441 to 10445 are as follows: Step 10441: Determine the structural characteristic constraint conditions according to the data storage structure characteristics of the first cloud platform and the data storage structure characteristics of the unclustered cloud platforms.

[0060] Optionally, the data storage structure characteristics in the embodiments of the present invention include the storage hierarchy structure, data access mode, and data redundancy strategy. Among them, the storage hierarchy structure (such as whether it is a multi-level storage, the type and capacity allocation of each level of storage), the data access mode (random access, sequential access, etc.), and the data redundancy strategy (full redundancy, incremental redundancy, etc.).

[0061] Therefore, the data management system compares the differences and commonalities of the data storage structure characteristics of the first cloud platform and the unclustered cloud platforms, and determines the structural characteristic constraint conditions applicable to the entire data disaster recovery scenario, ensuring that the selected cloud platforms can cooperate with each other in terms of data storage structure and meet the storage and recovery requirements of business data.

[0062] In one embodiment, the first cloud platform adopts a three-layer storage hierarchy structure. The upper layer is a high-speed solid-state storage for frequently accessed data, the middle layer is a mechanical hard disk storage for general data, and the lower layer is a tape library storage for long-term backup; the data access mode is mainly random access to meet the real-time requirements of the business; the data redundancy strategy is full redundancy to ensure the high reliability of the data. The unclustered cloud platform adopts a two-layer storage hierarchy structure. The upper layer is a hybrid storage (part solid-state and part mechanical hard disk), and the lower layer is a cloud storage; the data access mode has both random access and sequential access; the data redundancy strategy is incremental redundancy. Therefore, after comparing the differences and commonalities of the data storage structure characteristics, the following structural characteristic constraint conditions can be determined: the storage hierarchy structure should have at least two layers, and one of the layers should have a high read and write speed to meet the fast access of some data; the data access mode should support random access; the data redundancy strategy should ensure a certain reliability of the data, allowing full redundancy or incremental redundancy.

[0063] Step 10442: For each original clustering result in the first original clustering result and the second original clustering result, determine the characteristic similarity between any two cloud platforms according to the data storage structure characteristics of any two cloud platforms in the original clustering result.

[0064] Further, for each of the first original clustering result and the second original clustering result, the data management system traverses any two cloud platforms in the original clustering result. For the data storage structure characteristics of these any two cloud platforms, the similarity degree between any two cloud platforms is calculated respectively from three aspects: the storage hierarchy structure, the data access mode, and the data redundancy strategy. Among them, the similarity degree of the storage hierarchy structure can be calculated according to algorithms such as the tree edit distance algorithm and the hierarchical clustering algorithm; the similarity degree of the data access mode can be calculated according to algorithms such as the sequence alignment algorithm and the hidden Markov model (HMM); the similarity degree of the data redundancy strategy can be calculated according to algorithms such as the set similarity algorithm and the information entropy algorithm.

[0065] Further, for each original clustering result, the data management system comprehensively calculates the similarity degree of the storage hierarchy structure, the similarity degree of the data access mode, and the similarity degree of the data redundancy strategy between any two cloud platforms, and obtains the characteristic similarity between any two cloud platforms. Therefore, the degree of association between cloud platforms in terms of data storage structure can be better understood. The specific formula for the characteristic similarity is as follows: 。

[0066] Among them, represents the characteristic similarity, represents the similarity degree of the storage hierarchy structure, represents the similarity degree of the data access mode, represents the similarity degree of the data redundancy strategy.

[0067] In an embodiment, for any two cloud platforms, cloud platform A and cloud platform B, in the first original clustering result. The storage hierarchy structure of cloud platform A has two layers, with solid state in the upper layer and mechanical hard disk in the lower layer; the data access mode is random access; the data redundancy strategy is full - volume redundancy. The storage hierarchy structure of cloud platform B has two layers, with hybrid storage in the upper layer and cloud storage in the lower layer; the data access mode is random and sequential access; the data redundancy strategy is incremental redundancy. According to the corresponding algorithms, for calculating the similarity of the storage hierarchy structure: both have two layers and both have a high - speed storage layer (the solid - state layer of cloud platform A and the solid - state part of the hybrid storage of cloud platform B), and the similarity is 0.8. The similarity of the data access mode: both support random access, and the similarity is 0.7. The similarity of the data redundancy strategy: one is full - volume redundancy and the other is incremental redundancy, and the similarity is 0.5. Therefore, the characteristic similarity between cloud platform A and cloud platform B is 。

[0068] Optionally, embodiments of the present invention can also perform external correlation analysis based on the internal correlation degree of the data storage structure characteristics of the same cloud platform on different characteristic dimensions, and then combine the internal correlation degree between the data storage structure characteristics of different cloud platforms to determine the characteristic similarity between any two cloud platforms. Specifically: The data management system decomposes the data storage structure characteristics into different characteristic dimensions, such as storage hierarchy structure, data access mode, data redundancy strategy, etc. For the th cloud platform in each original clustering result, its characteristic value on the th characteristic dimension is , where , , represents the total number of cloud platforms in each original clustering result, and represents the total number of dimensions of the data storage structure characteristics.

[0069] Furthermore, the data management system analyzes the internal correlation degree of the data storage structure characteristics of the same cloud platform on different characteristic dimensions. For any two characteristic dimensions and , the internal correlation degree between any two characteristic dimensions and is: .

[0070] Among them, the internal correlation degree characterizes the mutual influence degree of the two characteristic dimensions and in the data storage structure of the cloud platform.

[0071] Furthermore, the data management system performs external correlation analysis based on the internal correlation degree between the data storage structure characteristics of different cloud platforms to determine the characteristic similarity between any two cloud platforms. Therefore, for the th cloud platform and the th cloud platform in each original clustering result, the th cloud platform and the th cloud platform, the specific formula for the characteristic similarity between them is: .

[0072] Step 10443, the characteristic association network constructed based on the characteristic similarity between any two cloud platforms divides the cloud platforms in the original clustering result into communities to obtain multiple clustering sub-results.

[0073] Furthermore, for each original clustering result, the data management system constructs a characteristic association network with cloud platforms as nodes and the characteristic similarity between any two cloud platforms as the edges between the nodes.

[0074] Further, the data management system uses a complex network community division algorithm (such as an improved version of the Louvain algorithm) to perform community division on the feature association network in combination with the feature similarity between any two cloud platforms, and divides the cloud platforms with relatively high feature similarity into the same clustering sub-result, so that the cloud platforms within each clustering sub-result are more similar in terms of data storage structure features, while the feature differences between cloud platforms in different clustering sub-results are relatively large, obtaining multiple clustering sub-results. Therefore, the cloud platforms can be classified more clearly, which is convenient for subsequent screening according to the structural feature constraint conditions.

[0075] In one embodiment, there are cloud platform A, cloud platform B, cloud platform C, and cloud platform D in the first original clustering result. In the constructed feature association network, the feature similarity between cloud platform A and cloud platform B is 0.7, between cloud platform A and cloud platform C is 0.5, between cloud platform A and cloud platform D is 0.4, between cloud platform B and cloud platform C is 0.6, between cloud platform B and cloud platform D is 0.5, and between cloud platform C and cloud platform D is 0.6. After performing community division using the improved Louvain algorithm, two clustering sub-results are obtained. One contains cloud platform A and cloud platform B, and the other contains cloud platform C and cloud platform D, because the feature similarity between cloud platform A and cloud platform B and between cloud platform C and cloud platform D is relatively high, while the similarity between cloud platform A, cloud platform B and cloud platform C, cloud platform D is relatively low.

[0076] Step 10444: Screen the cloud platforms in each clustering sub-result based on the structural feature constraint conditions to eliminate the cloud platforms whose data storage structure features do not meet the structural feature constraint conditions in each clustering sub-result, and obtain the optimized clustering result of the original clustering result.

[0077] Further, for each clustering sub-result, the data management system screens each cloud platform therein one by one according to the structural feature constraint conditions determined in step 10441, checks whether the data storage structure features of each cloud platform meet the structural feature constraint conditions. If not, the cloud platform is removed from the clustering sub-result to obtain the optimized result of each clustering sub-result.

[0078] Further, for each clustering sub-result in each original clustering result, the data management system integrates the optimized results of all clustering sub-results to obtain the optimized clustering result of each original clustering result. Therefore, the cloud platforms in the optimized clustering result all meet the overall requirements in terms of data storage structure features, improving the quality and usability of the clustering result.

[0079] In one embodiment, among the requirements of the structural characteristic constraints for at least two layers, there are cloud platform E and cloud platform F in a certain clustering sub-result. The storage hierarchical structure of cloud platform E has only one layer, which does not meet the requirement of at least two layers in the structural characteristic constraints. Therefore, cloud platform E is removed from this clustering sub-result. Cloud platform F meets all the structural characteristic constraints and is retained in the clustering sub-result.

[0080] Step 10445, determine the optimized clustering result with the largest number of cloud platforms as the target clustering result, and determine the cloud platforms in the target clustering result as the target cloud platforms.

[0081] Furthermore, the data management system compares the number of cloud platforms included in all the optimized clustering results, and determines the optimized clustering result with the largest number of cloud platforms as the target clustering result.

[0082] Furthermore, the data management system determines the cloud platforms in the target clustering result as the target cloud platforms. Among them, selecting the clustering result with the largest number enables the cloud platforms in this clustering result to have relatively high commonality and stability on the premise of meeting the structural characteristic constraints, and can provide broader and more reliable disaster recovery support for business data. In one embodiment, there are three optimized clustering results. The first one contains 3 cloud platforms, the second one contains 5 cloud platforms, and the third one contains 2 cloud platforms. Therefore, the second optimized clustering result is determined as the target clustering result, and the 5 cloud platforms in the clustering result are the target cloud platforms.

[0083] The embodiment of the present invention screens out the unqualified cloud platforms based on the constraints, improves the quality of the clustering result, and then determines the most representative and reliable target cloud platforms by selecting the optimized clustering result with the largest number of cloud platforms as the target clustering result. Overall, it can accurately screen out the most suitable cloud platforms for business data disaster recovery from the original clustering result, improve the effectiveness and stability of the disaster recovery plan, ensure the secure storage and efficient recovery of business data in different scenarios, and thus improve the stability and reliability of enterprise business operations.

[0084] In one embodiment, the descriptions of steps 301 to 304 are as follows: Step 301, perform an adaptability analysis based on the failure type of the primary device in combination with the data backup integrity status of each cloud platform in the target cloud platforms, and determine the adaptability index of each cloud platform in the target cloud platforms to the failure type.

[0085] Optionally, the data management system determines the type of failure of the master device. The type of failure may include hardware failure, software failure, network failure, etc. Different types of failures have different impacts on the integrity of data backup. Therefore, for each cloud platform in the target cloud platform corresponding to each type of business data, the data management system analyzes the adaptability of each cloud platform to various types of failures according to the data backup integrity status of each cloud platform, and determines the adaptability index of each cloud platform to the type of failure. The adaptability index characterizes the potential ability of the cloud platform to recover data under a specific type of failure. The data backup integrity status can be measured by the difference rate between the backup data and the latest business data. If the difference rate is lower, it means that the backup data is closer to the latest business data, and the adaptability in dealing with failures may be stronger. The specific formula for the adaptability index is as follows: 。

[0086] Wherein, represents the adaptability index of the th cloud platform in the target cloud platform to the type of failure , represents the data backup integrity status of the th cloud platform, represents the number of factors affecting adaptability, represents the weight of the th factor, represents the influence coefficient of the th influencing factor regarding the type of failure and the data backup integrity status . For example, for software failure, may be the influence coefficient of the integrity of critical business data, may be the influence coefficient of the consistency of data status before and after the failure occurs.

[0087] In one embodiment, a software failure occurs in the master device, which may cause some data to be incorrectly modified or lost. There are cloud platforms A, B, and C in the target cloud platform. The data backup integrity status of cloud platform A is 99% (i.e., the difference rate between the backup data and the latest business data is 1%), the data backup integrity status of cloud platform B is 95%, and the data backup integrity status of cloud platform C is 90%.

[0088] For software failure, the data management system analyzes that the higher the data backup integrity, the more advantageous it is in recovering data problems caused by software failure. After the adaptability analysis, the adaptability index of cloud platform A to software failure is 0.9, the adaptability index of cloud platform B to software failure is 0.7, and the adaptability index of cloud platform C to software failure is 0.5.

[0089] Step 302: Based on the severity of the master device's failure, perform a matching degree analysis in combination with the server load status and network latency status of each cloud platform in the target cloud platform to obtain the matching degree index of each cloud platform in the target cloud platform for the severity of the failure.

[0090] Furthermore, the data management system evaluates the severity of the master device's failure, which can be divided into levels such as minor, moderate, and severe. Therefore, for each cloud platform in the target cloud platform corresponding to each type of business data, the data management system performs a matching degree analysis based on the severity of the failure in combination with the server load status and network latency status of each cloud platform in the target cloud platform, analyzes the support ability of each cloud platform in the target cloud platform for business data recovery under different severities of the failure, and obtains the matching degree index of each cloud platform in the target cloud platform for the severity of the failure. Among them, the matching degree index characterizes the actual processing ability of the cloud platform under a specific severity of the failure. The server load status represents the proportion of the resources already used by the current server in the total resources. The lower the load, the more likely it is to be able to handle additional loads when processing data recovery requests. The network latency status represents the average network latency time between the cloud platform and the failed master device. The lower the latency, the faster the data can be transmitted back to the standby device. Among them, the specific formula for the matching degree index is as follows: 。

[0091] Among them, represents the matching degree index of the th cloud platform in the target cloud platform for the severity of the failure, represents the severity of the failure. When the severity of the failure is minor, ; when the severity of the failure is moderate, ; when the severity of the failure is severe, ; represents the server load status of the th cloud platform, represents the network latency status of the th cloud platform.

[0092] In an embodiment, the severity of the master device's failure is severe. For severe failures, lower server load and network latency are required to ensure rapid data recovery. The server load status of cloud platform A is 60%, that is, 0.6, and the network latency status is 50 ms; the server load status of cloud platform B is 40%, and the network latency status is 80 ms; the server load status of cloud platform C is 20%, and the network latency status is 100 ms.

[0093] After the matching degree analysis, the matching degree index of cloud platform A for severe failures is 0.6, the matching degree index of cloud platform B for severe failures is 0.4, and the matching degree index of cloud platform C for severe failures is 0.2.

[0094] Step 303: Perform a capability assessment based on the adaptability index and matching degree index of each cloud platform in the target cloud platform to determine the data recovery capability of each cloud platform in the target cloud platform for the service data of the master device.

[0095] Further, for each cloud platform in the target cloud platform corresponding to each type of service data, the management system comprehensively evaluates the data recovery capability of each cloud platform in the target cloud platform for the service data of the master device according to the adaptability index of each cloud platform to the fault type and the matching degree index to the fault severity. The specific formula for the data recovery capability is as follows: . Among them, represents the data recovery capability of the th cloud platform in the target cloud platform for the service data of the master device.

[0096] In an embodiment, for cloud platform A, its adaptability index is 0.9 and its matching degree index is 0.6. The adaptability index of cloud platform B is 0.7 and its matching degree index is 0.4. The adaptability index of cloud platform C is 0.5 and its matching degree index is 0.2. Therefore, the calculated data recovery capability of cloud platform A for the service data of the master device is 2.22, the data recovery capability of cloud platform B for the service data of the master device is 1.61, and the data recovery capability of cloud platform C for the service data of the master device is 1.01.

[0097] Step 304: Traverse the data recovery capabilities of each cloud platform in the target cloud platform, and determine the cloud platform with the maximum data recovery capability in the target cloud platform as the optimal data recovery cloud platform for each type of service data.

[0098] Further, for each cloud platform in the target cloud platform corresponding to each type of service data, the data management system traverses the data recovery capability values of each cloud platform in the target cloud platform, and determines the cloud platform with the maximum data recovery capability value as the optimal data recovery cloud platform for each type of service data. Continuing the above embodiment, the data recovery capability of cloud platform A for the service data of the master device is 2.22, the data recovery capability of cloud platform B for the service data of the master device is 1.61, and the data recovery capability of cloud platform C for the service data of the master device is 1.01. Therefore, cloud platform A is determined as the optimal data recovery cloud platform for each type of service data.

[0099] In the embodiment of the present invention, when a failure occurs in the master device, it can accurately find the most suitable cloud platform for recovering data for each type of service data, minimize the impact of the failure on the service to the greatest extent, ensure the integrity of the service data and the continuity of the service, and thus improve the stability and reliability of the enterprise service operation.

[0100] In one embodiment, the descriptions of steps 21 to 23 are as follows: Step 21, for each type of service data, perform an initial key mapping according to the platform ID information of the target cloud platform to generate an initial encryption key for the target cloud platform, and perform key expansion on the initial encryption key according to the character distribution characteristics of the platform ID information to obtain an expanded encryption key for the target cloud platform.

[0101] Optionally, the platform ID information in the embodiments of the present invention is in the form of a string. Therefore, for the platform ID information, it can be decomposed into multiple characters. Therefore, the platform ID information of the target cloud platform ID can be expressed as , where represents the number of characters in the platform ID information, represents the th character in the platform ID information. Therefore, the ASCII code value of each character in the platform ID information is taken to obtain . Therefore, for each target cloud platform corresponding to each type of service data, the data management system performs an initial key mapping according to the ASCII code value of each character in the platform ID information to generate an initial encryption key for the target cloud platform , and the specific formula is as follows: .

[0102] Furthermore, the data management system obtains the character length of the platform ID information and determines the key iteration times according to the character length , where the key iteration times , represents the character length, represents the floor operation. Furthermore, the data management system performs key iteration according to the key iteration times in combination with the character distribution characteristics of the platform ID information to perform key expansion on the initial encryption key to obtain an expanded encryption key for the target cloud platform. The above-mentioned initial encryption key is a 32-bit binary number. Therefore, the initial encryption key can be expressed as . For each iteration , times the key obtained from the iteration is divided into two 16-bit parts and . According to the ASCII code value of the th character in the platform ID information, calculate , , and then and are combined to obtain , the final expanded key is the result obtained in the th iteration .

[0103] Step 22: Divide the service data into multiple data blocks according to the parity of the number of characters in the platform ID information, and perform a permutation operation on the bytes in each data block in combination with the data block index value of each data block according to the character information of the characters in the platform ID information to obtain the permuted data block.

[0104] Furthermore, the data management system obtains the parity of the number of characters in the platform ID information, that is, whether the number of characters is odd or even, that is, to judge whether it holds.

[0105] Furthermore, the data management system determines the data block size according to the parity of the number of characters in the platform ID information, and divides the service data into multiple data blocks according to the data block size. Among them, each data block is established with a data block index value related to the platform ID information. In one embodiment, if holds, the data block size bytes, if does not hold, the data block size bytes, and divides the service data into multiple data blocks , among which, , among which, represents the number of bytes of the service data . For each data block , its data block index value is .

[0106] Furthermore, the data management system performs a permutation operation on the bytes in each data block in combination with the data block index value of each data block according to the character information of the characters in the platform ID information to obtain the permuted data block. Among them, the permutation rule combines the character order and numerical value of the platform ID information, so that the byte order of each data block is disrupted and closely associated with the platform ID. In one embodiment, for the data block , its byte sequence included is , and perform a permutation operation on each byte in the data block according to the permutation table to obtain the permuted data block of the data block , among which, in the permutation table , for each position in the permutation table , its corresponding value has the formula: .

[0107] Step 23: Encrypt the permuted data block based on the extended encryption key to obtain the finally encrypted data of the target cloud platform.

[0108] Further, the data management system encrypts the permuted data block based on the extended encryption key to obtain the finally encrypted data of the target cloud platform, as specifically described in Steps 2301 to 2304.

[0109] In the embodiment of the present invention, the service data is encrypted according to the platform ID information of the target cloud platform, enhancing the security of the data transmission process, avoiding interception and virus implantation and other intrusion operations during the data transmission process, and ensuring the security of enterprise services.

[0110] In one embodiment, the descriptions of Steps 2301 to 2304 are as follows: Step 2301: Sum the code values of the first preset-bit characters in the platform ID information to obtain a cyclic shift amount, multiply the code values of all characters in the platform ID information to obtain a mask value, and determine the number of cycles based on the number of characters in the platform ID information.

[0111] Optionally, the data management system sums the code values of the first preset-bit characters in the platform ID information and determines the cyclic shift amount according to the sum value. In the embodiment of the present invention, the first preset bit is, for example, 5 bits. Therefore, the sum value can be expressed as , and the cyclic shift amount is expressed as .

[0112] Further, the data management system multiplies the code values of all characters in the platform ID information and takes the lower 8 bits of the product value as the mask value.

[0113] Further, the data management system determines the number of cycles according to the number of characters in the platform ID information, where the number of cycles can be expressed as .

[0114] Step 2302: Perform a cyclic left shift operation on each byte in the permuted data block according to the cyclic shift amount to obtain a first encrypted data block, and perform a bitwise exclusive OR operation on each byte in the first encrypted data block with the mask value to obtain a second encrypted data block.

[0115] Further, the data management system performs a cyclic left shift operation on each byte in the permuted data block according to the cyclic shift amount to obtain a first encrypted data block.

[0116] Further, the data management system performs a bitwise exclusive OR operation on each byte in the first encrypted data block with the mask value to obtain a second encrypted data block.

[0117] Step 2303: Perform a cyclic operation on the second encrypted data block according to the number of cycles. In each cycle, shift the bytes in the second encrypted data block one position backward in sequence to obtain a third encrypted data block.

[0118] Further, the data management system performs a cyclic operation on the second encrypted data block according to the number of cycles. In each cycle, shift the bytes in the second encrypted data block one position backward in sequence (the last byte is shifted to the first position) to obtain a third encrypted data block.

[0119] Step 2304: Perform fusion based on the hash value of the extended encryption key and the platform ID information to obtain a final encryption key, and perform fusion with the final encryption key as the prefix and the third encrypted data block as the suffix to obtain the final encrypted data.

[0120] Further, the data management system extracts the key information of the platform ID information (such as the combination of the last 4 characters, the combination of the first 4 characters, the combination of the middle 4 characters, etc.), and performs a hash operation on the key information to obtain the hash value of the platform ID information. where the hash operation algorithm here is such as the MD5 algorithm, the SHA-1 algorithm, the SHA-256 algorithm, etc.

[0121] Further, the data management system performs fusion on the extended encryption key and the hash value of the platform ID information to obtain a final encryption key. Therefore, the final encryption key can be expressed as .

[0122] Further, the data management system performs fusion with the final encryption key as the prefix and the third encrypted data block as the suffix to obtain the final encrypted data.

[0123] The embodiment of the present invention encrypts business data, enhances the security of the data transmission process, avoids the interception and virus implantation of business data during the transmission process, and ensures the security of enterprise services.

[0124] Further, the cross-cloud platform disaster recovery data migration and recovery system provided by the present invention will be described below. The cross-cloud platform disaster recovery data migration and recovery system described below can be mutually corresponding and referred to with the cross-cloud platform disaster recovery data migration and recovery method described above.

[0125] Optionally, referring to Figure 2 , Figure 2 is the structural diagram of the cross-cloud platform disaster recovery data migration and recovery system provided by the present invention. The cross-cloud platform disaster recovery data migration and recovery system includes:

[0126] A cross-cloud platform determination module 210, configured to determine at least two target cloud platforms for each type of service data to be disaster-prepared in the master device according to the data characteristic information of each type of service data and the platform characteristic information of each cloud platform; A disaster recovery data migration module 220, configured to transmit each type of service data to at least two corresponding target cloud platforms for storage; A source cloud platform positioning module 230, configured to, when the master device fails, determine an optimal data recovery cloud platform for each type of service data according to the platform status information of each cloud platform among at least two corresponding target cloud platforms; A disaster recovery data recovery module 240, configured to recover each type of service data to a standby device of the master device based on each optimal data recovery cloud platform.

[0127] In the embodiment of the present invention, at least two target cloud platforms are selected for each type of service data as disaster recovery storage locations, avoiding the risk of centralized storage of service data in a single cloud platform. In the subsequent process, even if a certain cloud platform fails, the service data can still be recovered from other cloud platforms, avoiding the problem of service data loss and improving the reliability of enterprise business operations. On the other hand, when the master device fails, the optimal data recovery cloud platform is selected from multiple target cloud platforms according to the platform status information to recover each type of service data, avoiding the risk of service interruption, reducing the time in the service data recovery process, and improving the stability of enterprise business operations.

[0128] Please refer to Figure 3 , Figure 3 which is an embodiment diagram of the electronic device provided by the embodiment of the present invention. As Figure 3 shown, the embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented: For each type of service data to be disaster-prepared in the master device, determine at least two target cloud platforms for each type of service data according to the data characteristic information of each type of service data and the platform characteristic information of each cloud platform; Transmit each type of service data to at least two corresponding target cloud platforms for storage; When the master device fails, for each type of service data, determine an optimal data recovery cloud platform according to the platform status information of each cloud platform among at least two corresponding target cloud platforms; Recover each type of service data to a standby device of the master device based on each optimal data recovery cloud platform.

[0129] Please refer to Figure 4 , Figure 4This is an embodiment diagram of the computer-readable storage medium provided by the embodiments of the present invention. As Figure 4 shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 311 is stored. When the computer program 311 is executed by a processor, the following steps are implemented: For each type of service data to be disaster-prepared in the master device, at least two target cloud platforms for each type of service data are determined according to the data characteristic information of each type of service data and the platform characteristic information of each cloud platform; Each type of service data is transmitted to its corresponding at least two target cloud platforms for storage; When the master device fails, for each type of service data, an optimal data recovery cloud platform is determined according to the platform status information of each cloud platform in its corresponding at least two target cloud platforms; Based on each optimal data recovery cloud platform, each type of service data is restored to the standby device of the master device.

[0130] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the disaster recovery data migration and recovery method across cloud platforms provided by the above-mentioned various methods. The method includes: For each type of service data to be disaster-prepared in the master device, at least two target cloud platforms for each type of service data are determined according to the data characteristic information of each type of service data and the platform characteristic information of each cloud platform; Each type of service data is transmitted to its corresponding at least two target cloud platforms for storage; When the master device fails, for each type of service data, an optimal data recovery cloud platform is determined according to the platform status information of each cloud platform in its corresponding at least two target cloud platforms; Based on each optimal data recovery cloud platform, each type of service data is restored to the standby device of the master device.

[0131] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0132] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware cloud platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cross-cloud platform disaster recovery data migration and recovery method, characterized in that: include: For each type of business data to be prepared for disaster recovery in the primary device, determine at least two target cloud platforms for each type of business data based on data characteristic information of each type of business data and platform characteristic information of each cloud platform; Transmit each type of business data to at least two corresponding target cloud platforms for storage; When a master device fails, for each type of business data, the optimal data recovery cloud platform is determined according to the platform status information of each of the at least two corresponding target cloud platforms; Restore each type of business data to the backup device of the primary device based on each optimal data recovery cloud platform.

2. The cross-cloud platform disaster recovery data migration and recovery method according to claim 1 is characterized in that: The data characteristic information includes business type, importance and change frequency; the platform characteristic information includes storage capacity, read and write speed and security level; The step of determining at least two target cloud platforms for each type of business data according to the data characteristic information of each type of business data and the platform characteristic information of each cloud platform includes: Based on the security level of each cloud platform and the business type and importance of each type of business data, the first candidate cloud platform in the cloud platform library is determined; Determine a second candidate cloud platform among the first candidate cloud platforms based on matching the read and write speed of each cloud platform with the change frequency of each type of business data; Selecting a third candidate cloud platform from the second candidate cloud platforms based on the storage capacity of each cloud platform; Based on the coupling degree of each cloud platform pair in the third candidate cloud platform, at least two target cloud platforms for each type of business data are determined; the coupling degree of the cloud platform pair is determined based on the business association information and resource sharing interaction information of the cloud platform pair.

3. The cross-cloud platform disaster recovery data migration and recovery method according to claim 2 is characterized in that: The determining at least two target cloud platforms for each type of business data based on the coupling degree of each cloud platform pair in the third candidate cloud platforms includes: Taking any first cloud platform among the third candidate cloud platforms as an initial clustering center, classifying the second cloud platform among the third candidate cloud platforms into the cluster corresponding to the initial clustering center, and obtaining an initial clustering result of the first cloud platform; Taking the non-clustered cloud platform in the third candidate cloud platform as a new clustering center; Classifying the third cloud platform in the second cloud platform into the cluster corresponding to the new cluster center to obtain a first original clustering result of the unclustered cloud platform, and removing the fourth cloud platform in the third cloud platform from the initial clustering result to obtain a second original clustering result of the first cloud platform; Determining at least two target cloud platforms for each type of business data based on the first original clustering result and the second original clustering result; Among them, the coupling degree between the second cloud platform and the first cloud platform is less than or equal to a preset coupling threshold; the non-clustered cloud platform is a cloud platform other than the first cloud platform and the second cloud platform in the third candidate cloud platform; the coupling degree between the third cloud platform and the non-clustered cloud platform is less than or equal to the preset coupling threshold; the coupling degree deviation value between the coupling degree of the fourth cloud platform and the first cloud platform and the coupling degree with the non-clustered cloud platform is greater than the preset deviation threshold.

4. The cross-cloud platform disaster recovery data migration and recovery method according to claim 3 is characterized in that: The determining at least two target cloud platforms for each type of business data based on the first original clustering result and the second original clustering result includes: Determining structural characteristic constraints according to the data storage structural characteristics of the first cloud platform and the data storage structural characteristics of the non-clustered cloud platform; For each original clustering result in the first original clustering result and the second original clustering result, determining the characteristic similarity between any two cloud platforms according to the data storage structure characteristics of any two cloud platforms in the original clustering results; A feature association network constructed based on feature similarity between any two cloud platforms divides the cloud platforms in the original clustering results into communities to obtain multiple clustering sub-results; the feature association network is a topological network constructed with cloud platforms as nodes and feature similarities between cloud platforms as edges between nodes; Based on the structural characteristic constraint condition, the cloud platforms in each clustering sub-result are screened to eliminate the cloud platforms whose data storage structural characteristics do not satisfy the structural characteristic constraint condition in each clustering sub-result, so as to obtain the optimized clustering result of the original clustering result; The optimized clustering result containing the largest number of cloud platforms is determined as the target clustering result, and the cloud platform in the target clustering result is determined as the target cloud platform.

5. The cross-cloud platform disaster recovery data migration and recovery method according to claim 1, characterized in that: Determining the optimal data recovery cloud platform for each type of business data according to the platform status information of each cloud platform in at least two target cloud platforms corresponding to each type of business data includes: Based on the failure type of the main device and the data backup integrity status of each cloud platform in the target cloud platform, an adaptability analysis is performed to determine the adaptability index of each cloud platform in the target cloud platform to the failure type; Based on the fault severity of the main device, a matching degree analysis is performed in combination with the server load status and network delay status of each cloud platform in the target cloud platform to obtain a matching degree index of the fault severity of each cloud platform in the target cloud platform; Perform capability assessment based on the adaptability index and matching index of each cloud platform in the target cloud platform to determine the data recovery capability of each cloud platform in the target cloud platform for the business data of the primary device; The data recovery capability of each cloud platform in the target cloud platform is traversed, and the cloud platform with the largest data recovery capability in the target cloud platform is determined as the optimal data recovery cloud platform for each type of business data.

6. The cross-cloud platform disaster recovery data migration and recovery method according to any one of claims 1 to 5, characterized in that: After determining at least two target cloud platforms for each type of business data, the method further includes: For each type of business data, perform initial key mapping according to the platform ID information of the target cloud platform, generate the initial encryption key of the target cloud platform, and perform key expansion on the initial encryption key according to the character distribution characteristics of the platform ID information to obtain the expanded encryption key of the target cloud platform; Divide the service data into a plurality of data blocks according to the number parity of the characters in the platform ID information, and perform a permutation operation on the bytes in each data block according to the character information of the characters in the platform ID information combined with the data block index value of each data block to obtain a permuted data block; The replaced data block is encrypted based on the extended encryption key to obtain the final encrypted data of the target cloud platform.

7. The cross-cloud platform disaster recovery data migration and recovery method according to claim 6, characterized in that: The encrypting the replaced data block based on the extended encryption key to obtain the final encrypted data of the target cloud platform includes: Summing the code values ​​of the previously preset characters in the platform ID information to obtain a cyclic shift amount, multiplying the code values ​​of all characters in the platform ID information to obtain a mask value, and determining the number of cycles based on the number of characters in the platform ID information; Performing a cyclic left shift operation on each byte in the replaced data block according to the cyclic shift amount to obtain a first encrypted data block, and performing a bitwise XOR operation on each byte in the first encrypted data block and the mask value to obtain a second encrypted data block; Performing a loop operation on the second encrypted data block according to the number of loops, and moving the bytes in the second encrypted data block backward by one position in each loop, to obtain a third encrypted data block; The extended encryption key and the hash value of the platform ID information are merged to obtain a final encryption key, and the final encryption key is used as a prefix and the third encrypted data block is used as a suffix to obtain the final encrypted data.

8. A cross-cloud platform disaster recovery data migration and recovery system, characterized in that: The cross-cloud platform disaster recovery data migration and recovery method is applied to any one of claims 1 to 7; the cross-cloud platform disaster recovery data migration and recovery system comprises: A cross-cloud platform determination module is used to determine at least two target cloud platforms for each type of business data to be backed up in the primary device according to data characteristic information of each type of business data and platform characteristic information of each cloud platform; A disaster recovery data migration module is used to transfer each type of business data to at least two corresponding target cloud platforms for storage; A source cloud platform positioning module is used to determine the optimal data recovery cloud platform for each type of business data according to the platform status information of each cloud platform in at least two corresponding target cloud platforms when a main device fails; The disaster recovery data recovery module is used to restore each type of business data to the backup device of the main device based on each optimal data recovery cloud platform.

9. An electronic device, comprising: Memory for storing computer software programs; A processor, used to read and execute the computer software program, characterized in that when the processor executes the computer software program, it implements the disaster recovery data migration and recovery method across cloud platforms as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer software program stored therein, characterized in that: When the computer software program is executed by the processor, the cross-cloud platform disaster recovery data migration and recovery method as claimed in any one of claims 1 to 7 is implemented.

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